On Predicting Post-Click Conversion Rate via Counterfactual Inference

• URL: http://arxiv.org/abs/2510.04816v1
• Date: Mon, 06 Oct 2025 13:57:49 GMT
• Title: On Predicting Post-Click Conversion Rate via Counterfactual Inference
• Authors: Junhyung Ahn, Sanghack Lee,
• Abstract summary: We propose a method to counterfactually generate conversion labels for non-clicked samples.<n>Our approach is named the Entire Space Counterfactual Inference Multi-task Model (ESCIM)
• Score: 10.398909602421018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting conversion rate (CVR) is essential in various recommendation domains such as online advertising systems and e-commerce. These systems utilize user interaction logs, which consist of exposures, clicks, and conversions. CVR prediction models are typically trained solely based on clicked samples, as conversions can only be determined following clicks. However, the sparsity of clicked instances necessitates the collection of a substantial amount of logs for effective model training. Recent works address this issue by devising frameworks that leverage non-clicked samples. While these frameworks aim to reduce biases caused by the discrepancy between clicked and non-clicked samples, they often rely on heuristics. Against this background, we propose a method to counterfactually generate conversion labels for non-clicked samples by using causality as a guiding principle, attempting to answer the question, "Would the user have converted if he or she had clicked the recommended item?" Our approach is named the Entire Space Counterfactual Inference Multi-task Model (ESCIM). We initially train a structural causal model (SCM) of user sequential behaviors and conduct a hypothetical intervention (i.e., click) on non-clicked items to infer counterfactual CVRs. We then introduce several approaches to transform predicted counterfactual CVRs into binary counterfactual conversion labels for the non-clicked samples. Finally, the generated samples are incorporated into the training process. Extensive experiments on public datasets illustrate the superiority of the proposed algorithm. Online A/B testing further empirically validates the effectiveness of our proposed algorithm in real-world scenarios. In addition, we demonstrate the improved performance of the proposed method on latent conversion data, showcasing its robustness and superior generalization capabilities.

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